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57 lines (45 loc) · 1.91 KB
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'''Artificial neural network and noisy data with console output from p. 41 and p. 44 of the book.'''
import tensorflow as tf
import numpy as np
'''Adds a layer to the artificial neural network.'''
def add_layer(inputs,in_size,out_size,activation_function=None):
with tf.name_scope("layer"):
with tf.name_scope("weights"):
Weights = tf.Variable(tf.random_normal([in_size,out_size]))
with tf.name_scope("biases"):
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
with tf.name_scope("Wx_plus_b"):
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
'''Runs p. 41 artificial neural network code with p. 44 noisy data.'''
def main():
#Define placeholders for network inputs
with tf.name_scope("inputs"):
xs = tf.placeholder(tf.float32, [None,1],name="x_input")
ys = tf.placeholder(tf.float32, [None,1],name="y_input")
#Hidden Layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
#Output Layer
prediction = add_layer(l1, 10, 1, activation_function=None)
#Calculating loss
with tf.name_scope("loss"):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))
with tf.name_scope("train"):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session()
writer = tf.summary.FileWriter("logs/", sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
# P44 Noisy data
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = (x_data)**2 + noise
for i in range(1000):
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
main()